Open-source software for automated rodent behavioral analysis

被引:23
作者
Isik, Sena [1 ]
Unal, Gunes [1 ]
机构
[1] Bogazici Univ, Dept Psychol, Behav Neurosci Lab, Istanbul, Turkiye
关键词
behavioral analysis; open-source; object tracking; animal tracking; artificial intelligence; COMPUTATIONAL NEUROETHOLOGY; POSE ESTIMATION; TRACKING; IDENTIFICATION; SYSTEM; MICE;
D O I
10.3389/fnins.2023.1149027
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Rodent behavioral analysis is a major specialization in experimental psychology and behavioral neuroscience. Rodents display a wide range of species-specific behaviors, not only in their natural habitats but also under behavioral testing in controlled laboratory conditions. Detecting and categorizing these different kinds of behavior in a consistent way is a challenging task. Observing and analyzing rodent behaviors manually limits the reproducibility and replicability of the analyses due to potentially low inter-rater reliability. The advancement and accessibility of object tracking and pose estimation technologies led to several open-source artificial intelligence (AI) tools that utilize various algorithms for rodent behavioral analysis. These software provide high consistency compared to manual methods, and offer more flexibility than commercial systems by allowing custom-purpose modifications for specific research needs. Open-source software reviewed in this paper offer automated or semi-automated methods for detecting and categorizing rodent behaviors by using hand-coded heuristics, machine learning, or neural networks. The underlying algorithms show key differences in their internal dynamics, interfaces, user-friendliness, and the variety of their outputs. This work reviews the algorithms, capability, functionality, features and software properties of open-source behavioral analysis tools, and discusses how this emergent technology facilitates behavioral quantification in rodent research.
引用
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页数:12
相关论文
共 57 条
[1]  
ACHACOSO TB, 1990, PERSPECT BIOL MED, V33, P379
[2]   LORENZ,KONRAD - (1903-1989) - OBITUARY [J].
BATESON, P .
AMERICAN PSYCHOLOGIST, 1990, 45 (01) :65-66
[3]  
Bergstra J., 2013, P 12 PYTHON SCI C SC
[4]   DeepEthogram, a machine learning pipeline for supervised behavior classification from raw pixels [J].
Bohnslav, James P. ;
Wimalasena, Nivanthika K. ;
Clausing, Kelsey J. ;
Dai, Yu Y. ;
Yarmolinsky, David A. ;
Cruz, Tomas ;
Kashlan, Adam D. ;
Chiappe, M. Eugenia ;
Orefice, Lauren L. ;
Woolf, Clifford J. ;
Harvey, Christopher D. .
ELIFE, 2021, 10
[5]  
Cambridge Dictionary, 2023, EXPL CAMBR DICT
[6]  
Chen Z., 2020, bioRxiv, DOI DOI 10.1101/2020.12.04.405159
[7]   On the Firing Rate Dependency of the Phase Response Curve of Rat Purkinje Neurons In Vitro [J].
Couto, Joao ;
Linaro, Daniele ;
De Schutter, E. ;
Giugliano, Michele .
PLOS COMPUTATIONAL BIOLOGY, 2015, 11 (03)
[8]   PyGaze: An open-source, cross-platform toolbox for minimal-effort programming of eyetracking experiments [J].
Dalmaijer, Edwin S. ;
Mathot, Sebastiaan ;
Van der Stigchel, Stefan .
BEHAVIOR RESEARCH METHODS, 2014, 46 (04) :913-921
[9]   Computational Neuroethology: A Call to Action [J].
Datta, Sandeep Robert ;
Anderson, David J. ;
Branson, Kristin ;
Perona, Pietro ;
Leifer, Andrew .
NEURON, 2019, 104 (01) :11-24
[10]   Real-time analysis of the behaviour of groups of mice via a depth-sensing camera and machine learning [J].
de Chaumont, Fabrice ;
Ey, Elodie ;
Torquet, Nicolas ;
Lagache, Thibault ;
Dallongeville, Stephane ;
Imbert, Albane ;
Legou, Thierry ;
Le Sourd, Anne-Marie ;
Faure, Philippe ;
Bourgeron, Thomas ;
Olivo-Marin, Jean-Christophe .
NATURE BIOMEDICAL ENGINEERING, 2019, 3 (11) :930-942